TY - JOUR
T1 - Using Machine Learning to Predict Young People’s Internet Health and Social Service Information Seeking
AU - Adolescent Medicine Trials Network (ATN) CARES Team
AU - Comulada, W. Scott
AU - Goldbeck, Cameron
AU - Almirol, Ellen
AU - Gunn, Heather J.
AU - Ocasio, Manuel A.
AU - Fernández, M. Isabel
AU - Arnold, Elizabeth Mayfield
AU - Romero-Espinoza, Adriana
AU - Urauchi, Stacey
AU - Ramos, Wilson
AU - Rotheram-Borus, Mary Jane
AU - Klausner, Jeffrey D.
AU - Swendeman, Dallas
AU - Abdalian, Sue Ellen
AU - Bolan, Robert
AU - Bryson, Yvonne
AU - Cortado, Ruth
AU - Flynn, Risa
AU - Kerin, Tara
AU - Lightfoot, Marguerita
AU - Milburn, Norweeta
AU - Nielsen, Karin
AU - Reback, Cathy
AU - Rotheram-Borus, Mary Jane
AU - Tang, Wenze
AU - Rezvan, Panteha Hayati
AU - Weiss, Robert E.
N1 - Funding Information:
The members of Adolescent Medicine Trials Network CARES are Sue Ellen Abdalian, Elizabeth Mayfield Arnold, Robert Bolan, Yvonne Bryson, W. Scott Comulada, Ruth Cortado, M. Isabel Fernandez, Risa Flynn, Tara Kerin, Jeffrey Klausner, Marguerita Lightfoot, Norweeta Milburn, Karin Nielsen, Manuel Ocasio, Wilson Ramos, Cathy Reback, Mary Jane Rotheram-Borus, Dallas Swendeman, Wenze Tang, Panteha Hayati Rezvan, and Robert E. Weiss.
Funding Information:
CARES is a program project grant funded by the ATN for HIV/AIDS Interventions Research Program Grant at the National Institutes of Health (U19HD089886). The Eunice Kennedy Shriver National Institute of Child Health and Human Development is the primary funder of this network, with the support of the National Institute of Mental Health, National Institute of Drug Abuse, and National Institute on Minority Health and Health Disparities. Additional support was provided by the National Institute of Mental Health through the Center for HIV Identification, Prevention, and Treatment Services (CHIPTS; P30MH058107) and an HIV training grant (T32MH109205).
Publisher Copyright:
© 2021, Society for Prevention Research.
PY - 2021/11
Y1 - 2021/11
N2 - Machine learning creates new opportunities to design digital health interventions for youth at risk for acquiring HIV (YARH), capitalizing on YARH’s health information seeking on the internet. To date, researchers have focused on descriptive analyses that associate individual factors with health-seeking behaviors, without estimating of the strength of these predictive models. We developed predictive models by applying machine learning methods (i.e., elastic net and lasso regression models) to YARH’s self-reports of internet use. The YARH were aged 14–24 years old (N = 1287) from Los Angeles and New Orleans. Models were fit to three binary indicators of YARH’s lifetime internet searches for general health, sexual and reproductive health (SRH), and social service information. YARH responses regarding internet health information seeking were fed into machine learning models with potential predictor variables based on findings from previous research, including sociodemographic characteristics, sexual and gender minority identity, healthcare access and engagement, sexual behavior, substance use, and mental health. About half of the YARH reported seeking general health and SRH information and 26% sought social service information. Areas under the ROC curve (≥.75) indicated strong predictive models and results were consistent with the existing literature. For example, higher education and sexual minority identification was associated with seeking general health, SRH, and social service information. New findings also emerged. Cisgender identity versus transgender and non-binary identities was associated with lower odds of general health, SRH, and social service information seeking. Experiencing intimate partner violence was associated with higher odds of seeking general health, SRH, and social service information. Findings demonstrate the ability to develop predictive models to inform targeted health information dissemination strategies but underscore the need to better understand health disparities that can be operationalized as predictors in machine learning algorithms.
AB - Machine learning creates new opportunities to design digital health interventions for youth at risk for acquiring HIV (YARH), capitalizing on YARH’s health information seeking on the internet. To date, researchers have focused on descriptive analyses that associate individual factors with health-seeking behaviors, without estimating of the strength of these predictive models. We developed predictive models by applying machine learning methods (i.e., elastic net and lasso regression models) to YARH’s self-reports of internet use. The YARH were aged 14–24 years old (N = 1287) from Los Angeles and New Orleans. Models were fit to three binary indicators of YARH’s lifetime internet searches for general health, sexual and reproductive health (SRH), and social service information. YARH responses regarding internet health information seeking were fed into machine learning models with potential predictor variables based on findings from previous research, including sociodemographic characteristics, sexual and gender minority identity, healthcare access and engagement, sexual behavior, substance use, and mental health. About half of the YARH reported seeking general health and SRH information and 26% sought social service information. Areas under the ROC curve (≥.75) indicated strong predictive models and results were consistent with the existing literature. For example, higher education and sexual minority identification was associated with seeking general health, SRH, and social service information. New findings also emerged. Cisgender identity versus transgender and non-binary identities was associated with lower odds of general health, SRH, and social service information seeking. Experiencing intimate partner violence was associated with higher odds of seeking general health, SRH, and social service information. Findings demonstrate the ability to develop predictive models to inform targeted health information dissemination strategies but underscore the need to better understand health disparities that can be operationalized as predictors in machine learning algorithms.
KW - Digital health intervention
KW - HIV
KW - Internet health information
KW - Machine learning
KW - Social service information
UR - http://www.scopus.com/inward/record.url?scp=85105866157&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85105866157&partnerID=8YFLogxK
U2 - 10.1007/s11121-021-01255-2
DO - 10.1007/s11121-021-01255-2
M3 - Article
C2 - 33974226
AN - SCOPUS:85105866157
SN - 1389-4986
VL - 22
SP - 1173
EP - 1184
JO - Prevention Science
JF - Prevention Science
IS - 8
ER -